SoroushVahidi/scidocs huggingface dataset:
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
(2018)

In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks. The extrapolation scheme is a classical approach for accelerating stochastic gradient descent for convex optimization, but it does not work well for nonconvex optimization typically. Alternatively, we propose an interpolation scheme to accelerate nonconvex optimization and call the method Interpolatron. We explain motivation behind Interpolatron and conduct a thorough empirical analysis. Empirical results on DNNs of great depths (e.g., 98-layer ResNet and 200-layer ResNet) on CIFAR-10 and ImageNet show that Interpolatron can converge much faster than the stateof-the-art methods such as the SGD with momentum and Adam. Furthermore, Anderson’s acceleration, in which mixing coefficients are computed by least-squares estimation, can also be used to improve the performance. Both Interpolatron and Anderson’s acceleration are easy to implement and tune. We also show that Interpolatron has linear convergence rate under certain regularity assumptions.
How Does He Saw Me ? A Recommendation Engine for Picking Heroes in Dota 2
(2013)

In this paper, we present a hero recommendation engine for the popular computer game Dota 2. We detail previous efforts at hero recommendation, building on these to provide a more complete exploration of machine learning algorithms applied to this problem. In addition to discussing the details behind the machine learning algorithms we use, we also provide insight into our method of data collection and feature selection. In doing so, the outcome of our efforts is two-fold. First, we provide the first public survey of machine learning algorithms applied to Dota 2–in the process gaining further insight into the algorithms we explore. Second, we provide a tool that we believe will be useful for both the casual and competitive sides of Dota 2’s 6 million unique player base.
Simultaneous penile-vaginal intercourse orgasm is associated with satisfaction (sexual, life, partnership, and mental health).
(2010)

INTRODUCTION
Previous multivariate research found that satisfaction was associated positively with frequency of specifically penile-vaginal intercourse (PVI; as opposed to other sexual activities) as well as with vaginal orgasm. The contribution to satisfaction of simultaneous orgasm produced by PVI merited direct examination in a large representative sample.


AIMS
To examine the associations of aspects of satisfaction (sexual, life, own mental health, partner relationship) with consistency of simultaneous orgasm produced by PVI (as well as with PVI frequency and vaginal orgasm consistency).


METHODS
A representative sample of Czechs (N = 1,570) aged 35-65 years completed a survey on aspects of satisfaction, PVI frequency, vaginal orgasm consistency, and consistency of simultaneous orgasm produced by PVI (the latter being a specially timed version of vaginal orgasm for women).


MAIN OUTCOME MEASURES
Analysis of variance of satisfaction components (LiSat scale items) from age and the sexual behaviors.


RESULTS
For both sexes, all aspects of satisfaction were associated with simultaneous PVI orgasm consistency and with PVI frequency (except female life satisfaction). All aspects of satisfaction were also associated with vaginal orgasm consistency. Multivariate analyses indicated that PVI frequency and simultaneous orgasm consistency make independent contributions to the aspects of satisfaction for both sexes.


CONCLUSIONS
For both sexes, PVI frequency and simultaneous orgasm produced by PVI (as well as vaginal orgasm for women) are associated with greater life, sexual, partnership, and mental health satisfaction. Greater support for these specific aspects of sexual activity is warranted.
Note-Taking and Secondary Students with Learning Disabilities : Challenges and Solutions
(2013)

As more secondary students with learning disabilities (LD) enroll in advanced content-area classes and are expected to pass state exams, they are faced with the challenge of mastering difficult concepts and abstract vocabulary while learning content. Once in these classes, students must learn from lectures that move at a quick pace, record accurate and complete notes, and then demonstrate their mastery of the content on tests. This article provides an overview of the challenges faced by students with LD in content-area classes and discusses the problems that students have learning from lectures and recording notes. Further, the article discusses theory and research related to note-taking and presents note-taking interventions that teachers can use to help students improve their note-taking skills, and ultimately, improve their achievement in these classes.
The evolutionary impact of invasive species.
(2001)

Since the Age of Exploration began, there has been a drastic breaching of biogeographic barriers that previously had isolated the continental biotas for millions of years. We explore the nature of these recent biotic exchanges and their consequences on evolutionary processes. The direct evidence of evolutionary consequences of the biotic rearrangements is of variable quality, but the results of trajectories are becoming clear as the number of studies increases. There are examples of invasive species altering the evolutionary pathway of native species by competitive exclusion, niche displacement, hybridization, introgression, predation, and ultimately extinction. Invaders themselves evolve in response to their interactions with natives, as well as in response to the new abiotic environment. Flexibility in behavior, and mutualistic interactions, can aid in the success of invaders in their new environment.
International Accounting Standards and Accounting Quality
(2005)

We compare characteristics of accounting amounts for firms that apply International Accounting Standards (IAS) to a matched sample of firms that do not to investigate whether applying IAS is associated with higher accounting quality and lower equity cost of capital. We find that firms applying IAS evidence less earnings management, more timely loss recognition, and more value relevance of accounting amounts than do those applying domestic GAAP. Firms applying IAS have higher variance of the change in net income, a higher ratio of the variances of the change in net income and change in cash flows, a significantly less negative correlation between accruals and cash flows, and a lower frequency of small positive net income. They have a significantly higher frequency of large negative net income and generally higher value relevance of accounting amounts. Differences between firms applying IAS and those applying domestic GAAP in the period before IAS firms adopt IAS do not explain the differences in accounting quality. Firms applying IAS generally exhibit higher accounting quality than when they previously applied domestic GAAP. The increase in accounting quality for IAS firms is generally greater than that for firms applying domestic GAAP throughout the sample period. We also find weak evidence suggesting that application of IAS is associated with a lower equity cost of capital. Overall, our results suggest improvement in accounting quality associated with applying IAS.
As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian
(2016)

Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally. They are often used in everyday communication and are an important part of cultural heritage. Having an up-to-date corpus of similes is challenging, as they are constantly coined and/or adapted to the contemporary times. In this paper we present a methodology for semi-automated collection of similes from the world wide web using text mining techniques. We expanded an existing corpus of traditional similes (containing 333 similes) by collecting 446 additional expressions. We, also, explore how crowdsourcing can be used to extract and curate new similes.
The Information Systems Identity Crisis: Focusing on High-Visibility and High-Impact Research
(2005)

This paper presents an alternative view of the Information Systems identity crisis described recently by Benbasat and Zmud (2003). We agree with many of their observations, but we are concerned with their prescription for IS research. We critique their discussion of errors of inclusion and exclusion in IS research and highlight the potential misinterpretations that are possible from a literal Ron Weber was the accepting senior editor for this paper. Iris Vessey and Rudy Hirschheim served as reviewers. reading of their comments. Our conclusion is that following Benbasat and Zmud’s nomological net will result in a micro focus for IS research. The results of such a focus are potentially dangerous for the field. They could result in the elimination of IS from many academic programs. We present an alternative set of heuristics that can be used to assess what lies within the domain of IS scholarship. We argue that the IS community has a powerful story to tell about the transformational impact of information technology. We believe that a significant portion of our research should be macro studies of the impact of IT. It is important for academic colleagues, deans, and managers to understand the transformational power of the technology. As IS researchers with deep knowledge of the underlying artifact, we are best positioned to do such research.
Object-Oriented Bayesian Networks
(1997)

Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies. Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN—particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts—can also be used to speed up the inference process.
Entrapment of the sciatic nerve at the linea aspera: A case report and literature review
(2016)

BACKGROUND
Nontraumatic, non-neoplastic sciatic nerve entrapment at the level of the thigh is extremely rare. In its course, in proximity of the linea aspera, the nerve is exposed to unexpected neuropathic syndromes associated with bone disorders.


CASE DESCRIPTION
A 67-year-old woman presented with a painful, neuropathic syndrome of the sciatic nerve, not resulting from any trauma and persisting for approximately 2 years. Imaging studies of the thigh showed a delimited zone of hyperostosis in the proximal third of the femoral diaphysis. The symptoms dramatically resolved after the patient underwent neurolysis of the tract of the nerve adjoining to the linea aspera. At the clinical checkup 2 years later, the patient remained free of pain.


CONCLUSION
The diagnosis of sciatic nerve entrapment at the linea aspera may present considerable difficulties. The clinical history and physical examination sometimes motivate the exploration and neurolysis of the nerve at this site.
Leveraging Peer Centrality in the Designof Socially-Informed Peer-to-Peer Systems
(2014)

Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. We demonstrate with an application scenario on real social networks the usability of the projection graph properties in designing social search applications and unstructured P2P overlays that exhibit improved performance and reduced overhead over baseline scenarios.
The Coherent Heart Heart – Brain Interactions , Psychophysiological Coherence , and the Emergence of System-Wide Order
(2006)

This article presents theory and research on the scientific study of emotion that emphasizes the importance of coherence as an optimal psychophysiological state. A dynamic systems view of the interrelations between psychological, cognitive and emotional systems and neural communication networks in the human organism provides a foundation for the view presented. These communication networks are examined from an information processing perspective and reveal a fundamental order in heart-brain interactions and a harmonious synchronization of physiological systems associated with positive emotions. The concept of coherence is drawn on to understand optimal functioning which is naturally reflected in the heart’s rhythmic patterns. Research is presented identifying various psychophysiological states linked to these patterns, with neurocardiological coherence emerging as having significant impacts on well being. These include psychophysiological as well as improved cognitive performance. From this, the central role of the heart is explored in terms of biochemical, biophysical and energetic interactions. Appendices provide further details and research on; psychophysiological functioning, reference previous research in this area, details on research linking coherence with optimal cognitive performance, heart brain synchronization and the energetic signature of the various psychophysiological modes.
Preliminary, open-label, pilot study of add-on oral Δ9-tetrahydrocannabinol in chronic post-traumatic stress disorder.
(2014)

BACKGROUND AND OBJECTIVES
Many patients with post-traumatic stress disorder (PTSD) achieve but partial remission with current treatments. Patients with unremitted PTSD show high rates of substance abuse. Marijuana is often used as compassion add-on therapy for treatment-resistant PTSD. This open-label study evaluates the tolerance and safety of orally absorbable Δ(9)-tetrahydrocannabinol (THC) for chronic PTSD.


METHODS
Ten outpatients with chronic PTSD, on stable medication, received 5 mg of Δ(9)-THC twice a day as add-on treatment.


RESULTS
There were mild adverse effects in three patients, none of which led to treatment discontinuation. The intervention caused a statistically significant improvement in global symptom severity, sleep quality, frequency of nightmares, and PTSD hyperarousal symptoms.


CONCLUSIONS
Orally absorbable Δ(9)-THC was safe and well tolerated by patients with chronic PTSD.
Subjective Evaluation of Latency and Packet Loss in a Cloud-Based Game
(2013)

On-demand multimedia services are more popular than ever and continue to grow. Consumers can now stream music, movies, television, and video games at the push of a button. Such services typically require a minimum connection speed to support streaming. However, transient network effects such as packet loss and delay variation can play a crucial role in determining the user quality of experience (QoE) in streaming multimedia systems. This paper will seek to establish the subjective impact of negative network effects on the user experience of a popular cloud-based on-demand video game service.
Privacy preservation and public auditing for cloud data using ASS in Multi-cloud
(2015)

Cloud computing is the new business paradigm introduced in the IT market in the current decade providing the customers with the option to pay for the amount of service utilized. The use of cloud computing has increased rapidly in many organizations. Ensuring security of data in a cloud is a major factor in the cloud computing environment, as users often store fragile information in cloud storage. Public auditing on shared data stored in the cloud is done by exploiting Aggregate Signature Scheme. With this mechanism, the identity of the signer of various blocks of shared data is kept private from public verifiers, who are able to verify efficiently the integrity of shared data without retrieving the entire file. However traceability is a major issue. A Multi-clouds Database Model (MCDB) which is based on Multi-clouds service providers instead of using single cloud service provider such as in Amazon cloud service is used to implement traceability. Also, dealing with single cloud providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards multi-clouds is highly efficient and this system would also check for the data's which are attacked by malicious users, with short time and cost requirement.
Event-Driven Semantic Concept Discovery by Exploiting Weakly Tagged Internet Images
(2014)

Analysis and detection of complex events in videos require a semantic representation of the video content. Existing video semantic representation methods typically require users to pre-define an exhaustive concept lexicon and manually annotate the presence of the concepts in each video, which is infeasible for real-world video event detection problems. In this paper, we propose an automatic semantic concept discovery scheme by exploiting Internet images and their associated tags. Given a target event and its textual descriptions, we crawl a collection of images and their associated tags by performing text based image search using the noun and verb pairs extracted from the event textual descriptions. The system first identifies the candidate concepts for an event by measuring whether a tag is a meaningful word and visually detectable. Then a concept visual model is built for each candidate concept using a SVM classifier with probabilistic output. Finally, the concept models are applied to generate concept based video representations. We use the TRECVID Multimedia Event Detection (MED) 2013 as our video test set and crawl 400K Flickr images to automatically discover 2, 000 visual concepts. We show significant performance gains of the proposed concept discovery method over different video event detection tasks including supervised event modeling over concept space and semantic based zero-shot retrieval without training examples. Importantly, we show the proposed method of automatic concept discovery outperforms other well-known concept library construction approaches such as Classemes and ImageNet by a large margin (228%) in zero-shot event retrieval. Finally, subjective evaluation by humans also confirms clear superiority of the proposed method in discovering concepts for event representation.
Unsupervised Modeling of Twitter Conversations
(2010)

We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.
Neural Responding Machine for Short-Text Conversation
(2015)

We propose Neural Responding Machine (NRM), a neural networ k-based response generator for Short-Text Conversation. NRM takes the general encoder-de coder framework: it formalizes the generation of response as a decoding process based on the lat ent representation of the input text, while both encoding and decoding are realized with recurren t n ural networks (RNN). The NRM is trained with a large amount of one-round conversation dat a collected from a microblogging service. Empirical study shows that NRM can generate gramma tically correct and content-wise appropriate responses to over 75% of the input text, outperf orming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.
Development of an OCV prediction mechanism for lithium-ion battery system
(2013)

This paper investigates the development of an open circuit voltage (OCV) prediction technique for lithium cells. The underlying principal of the technique described in this paper employs one simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and verified by using results obtained from experiments conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales. Lithium-ion battery system analysis was carried out with reference to the standard battery system models available in the literature.
'Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors
(2011)

Electronic medical record (EMR) systems can deliver many benefits to healthcare organizations and the patients they serve. However, one of the biggest stumbling blocks in garnering these benefits is the limited adoption and use by doctors. We employ the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation and adapt the theory to the context of EMR system adoption and use by doctors. Specifically, we suggest that age will be the only significant moderator, and gender, voluntariness and experience will not play significant moderating roles. We tested our model in a longitudinal study over a 7-month period in a hospital implementing a new EMR system. We collected 3 waves of survey data from 141 doctors and used system logs to measure use. While the original UTAUT only predicted about 20% of the variance in intention, the modified UTAUT predicted 44%. Both models were comparable in their prediction of use. In addition to contributing to healthcare IT and UTAUT research, we hope this work will serve as a foundation for future work that integrates UTAUT with other theoretical perspectives.